Why vehicle logistics can’t afford to drive blind anymore

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Why vehicle logistics can’t afford to drive blind anymore

  • Why vehicle logistics can’t afford to drive blind anymore
2 minute read

Availability has given way to affordability. Profit margins are shrinking. Production forecasts are being whittled down. Demand is losing steam. From raw material shortages to rising energy prices, the automotive industry is facing an avalanche of disruptions. Beneath the weight of these pressures lies an inflexible legacy logistics system that is creaking, every supply chain disruption threatening to bring everything to a grinding halt.

At Automotive Logistics & Supply Chain Global 2025, top supply chain leaders gathered to discuss how logistics and supply chain teams can build more efficient, resilient, and digitally connected networks that withstand volatility 
and disruption.

Key challenges in vehicle logistics

Market volatility

Not only is consumer demand for vehicles constantly shifting, but consumer preferences are changing too. With buyers choosing between EVs, hybrids, and ICE vehicles, companies are finding it difficult to predict demand accurately. Complex regulations, shifting trade policies, tariff uncertainty, and geopolitical tensions further exacerbate the turbulence. These rapid changes make it increasingly difficult for automakers to balance production with actual demand, resulting in overstocked yards in some regions and shortages in others.

Silos in decision-making

Every department is operating in silos and pulling in a different direction, using traditional methods that block collaboration. Vehicle Identification Numbers (VINs) often sit idle for days at distribution centers due to missing parts. Dealers, meanwhile, have little to no visibility into when those VINs will arrive, especially when vessels or trucks face delays.

Inbound orders parts without considering that production is operating on a downgraded forecast. Outbound schedules 200 trucks, while production has only completed 150 vehicles due to missing parts. When inbound, outbound, transportation, warehousing, production, and planning each act independently, decisions are made in silos, driving costs, increasing delays, and eroding efficiency across the chain.

Operational inefficiency

Beyond the silos, inefficiencies run deep within each operation. Many OEMs and Tier 1 suppliers still rely on outdated systems and manual processes, which slow down workflows and increase the likelihood of human error. With every department working on its own timeline and using disconnected tools, even minor bottlenecks can cause ripple effects, delaying deliveries, increasing freight and labor costs, and reducing overall productivity. These inefficiencies ultimately squeeze profit margins further, making it more difficult for automakers to remain agile.
 

Taking steps toward flexible vehicle logistics

Strengthening collaboration across the network

OEMs are adopting technologies that enable stronger collaboration and information sharing across multi-tier networks. This builds end-to-end visibility across suppliers, 3PLs, logistics providers, and dealers. The earlier disruptions are detected, the more response options become available, helping reduce costs and improve service reliability.
 

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Planning for uncertainty with scenario modeling

To manage volatility and global uncertainty—from tariffs to port closures—automakers are investing in scenario and simulation planning capabilities that help them prepare for multiple outcomes. By modeling “what-if” situations such as insourcing half their freight, or a 20% surge in hybrid demand, companies can evaluate the impact on cost, margins, and CO₂ goals. These simulations also surface potential logistics capacity issues in advance, allowing decision-makers to select the strategies that best balance profitability, service, and sustainability.

Embedding flexibility across strategic, planning, and execution horizons

Flexibility in vehicle logistics isn’t a single initiative; it must be built into every layer of the supply chain.

  • Strategic horizon: Flexibility begins at the network-design level. By combining scenario planning, network modeling, and simulation, manufacturers can anticipate supply disruptions, evaluate alternate routes and carriers, and stress-test their networks against geopolitical or tariff changes.
  • Planning horizon: Flexibility must be built into the planning process itself. By incorporating transportation and warehouse constraints directly into sales and operations, companies can model multiple demand and supply realizations. This ensures plans are executable and aligned with actual capacity, critical when balancing the mix between EV, hybrid, and ICE production.
  • Execution Horizon: When disruptions occur, flexibility depends on real-time visibility and responsiveness. Whether it’s semiconductors delayed in one region or missing components from another, execution-level scenario planning helps teams reroute shipments, expedite orders, or select alternative suppliers and carriers instantly, keeping production on schedule.

Connecting planning and execution on a unified platform

Leading manufacturers are turning to connected planning and execution platforms that unify inbound, outbound, warehousing, and transportation. This integration enables end-to-end visibility, reduces lead times, and improves agility across the network. For example, if a vessel carrying parts or finished vehicles is delayed, the system can automatically re-slot production, update warehouse work orders, adjust load plans, and retender shipments in real time. The result is accurate ETAs for dealers and customers, improved delivery performance, and a more resilient logistics operation.

The role of intelligent agents

Data is the foundation of intelligent logistics. A strong data strategy must begin with specific use cases and measurable outcomes that organizations want to achieve.

With today’s technology, logistics teams no longer need to choose between speed and accuracy. AI, machine learning, and intelligent agents make it possible to have both. These capabilities are now supporting a wide range of use cases that drive visibility, predictability, and automation across the automotive supply chain.

  • Autonomous tuning: Predicting and dynamically adjusting supply chain parameters such as transportation and lead times. This helps detect issues like broken bills of materials, invalid order quantities, or missing network definitions early.
  • Disruption prediction: Leveraging AI/ML to sense and forecast potential supply chain disruptions—even when future data isn’t available. This includes identifying risks across multiple tiers, whether it’s a truck on the road or a vessel at sea, and mapping the downstream impact all the way to dealers or customers.
  • Collaboration enablement: One of the biggest challenges in working with suppliers, 3PLs, and carriers is onboarding and data sharing. Modern hub-to-hub collaboration frameworks are helping manufacturers and logistics partners connect seamlessly, share information securely, and work from a common source of truth.


Beyond these core capabilities, AI agents are redefining daily logistics operations:

  • Inventory Agents identify causes of fulfillment delays and stockouts.
  • Warehouse Agents detect inefficiencies in picking, slotting, and inbound receiving.
  • Logistics Agents optimize routes, reduce empty miles, and improve fleet utilization.
  • Tariff Agents assess duty exposure and model alternative sourcing or routing options.
  • Generative AI Assistants summarize data, highlight exceptions, and support scenario planning through natural-language interaction.


Looking ahead

Volatility is expected to intensify in the years ahead. The way forward for automotive manufacturers is to invest in technologies that enhance the tensile strength of their supply chains, systems that enable flexibility through connected networks, real-time visibility, and AI-driven decision-making.